
The Data: combination of historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.
You are provided hourly rental data spanning two years. The data set is comprised of the first 19 days of each month.
Before we begin predicting anything we will analyse the data to gain a better understanding and try to identify some of the key trands and possible decision variables.
#Installing Packages
!pip install pandas
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
%matplotlib inline
import os
os.getcwd()
#https://plot.ly/python/offline/
'C:\\Users\\marcus.ohanlon\\Notebooks'
import plotly as py
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
import cufflinks as cf
from plotly.graph_objs import *
init_notebook_mode()
import datetime as dt
cf.go_offline()
cf.set_config_file(theme='white')